1. Field of the Invention
The invention relates to an antenna processing method for potentially non-centered or centered cyclostationary sources.
It can be applied for example to CPFSK sources with integer modulation index.
The invention relates, for example, to a method for the separation of potentially non-centered, cyclostationary signals received by a receiver of a communications system comprising several sources or emitters. The term “cyclostationary” also designates the particular case of stationary signals.
It can also be applied to the angular localization or goniometry of potentially non-centered cyclostationary sources.
The invention can be applied especially in radiocommunications, space telecommunications or passive listening to these links, in frequencies ranging from the VLF (Very Low Frequency) to the EHF (Extremely High Frequency).
In the present description, the term “blind separation” designates the separation of emitters with no knowledge whatsoever of the signals sent, the term “centered signal” refers to a signal without any continuous component that verifies E[x(t)]=0, and the term “non-stationary signal” refers to a signal whose statistics are time-dependent.
2. Description of the Prior Art
In many contexts of application, the reception of signals of interest for the receiver is very often disturbed by the presence of other signals (or sources) known as parasites, which may correspond either to delayed versions of the signals of interest (through multiple-path propagation), or to interfering sources which may be either deliberate or involuntary (in the case of co-channel transmissions). This is especially the case with radiocommunications in urban areas, subject to the phenomenon of multiple paths resulting from the reflections of the signal on surrounding fixed or moving obstacles potentially disturbed by the co-channel transmissions coming from the neighboring cells that re-use the same frequencies (in the case of F/TDMA or Frequency/Time Division Multiple Access networks). This is also the case with the HF (High Frequency) ionospherical links disturbed by the presence, at reception, of the multiple paths of propagation resulting from the reflections on the different ionospherical layers and of parasitic emitters due to high spectral congestion in the HF range.
For all these applications, whether it is for purposes of radiocommunications or for listening and technical analysis of the sources received, the sources need to be separated before other processing operations specific to the application considered are implemented. Furthermore, for certain applications such as passive listening, the sources received are totally unknown to the receiver (there are no available learning sequences, the waveforms are unknown, etc.) and their angular localization or goniometry may prove to be difficult (because of coupling between sensors) or costly (because of the calibration of the aerials) to implement. This is why it may prove to be highly advantageous to implement a source separation technique in a totally autodidactic or self-learning way, that is, by making use of no a priori information on the sources, apart from the assumption of the statistical independence of these sources.
The first studies on the separation of sources by self-learning appeared in the mid-1980s in the work of Jutten and Herault [1]. Since then, these studies have been constantly developing for mixtures of sources, both convolutive (time-spread multiple-path propagation channels) and instantaneous (distortion-free channels). A conspectus of these studies is presented in the article [2] by P. Comon and P. Chevalier. A certain number of techniques developed are called second-order techniques because they use only the information contained in the second-order statistics of the observations, as described in reference [3] for example. By contrast, other techniques, known as higher-order techniques, described for example in the reference [4], generally use not only second-order information but also information contained in statistics above the second order. These include the techniques known as cumulant-based, fourth-order techniques which have received special attention owing to their performance potential (reference [2]) and the relative simplicity of their implementation.
However, almost all the techniques of self-learned source separation available to date have been designed to separate sources assumed to be stationary, centered and ergodic, on the basis of estimators of statistics of observations qualified as being empirical, asymptotically unbiased and consistent on the basis of the above assumptions.
Two families of second-order separators are presently available. Those of the first family (F1) (reference [3], using the SOBI method shown schematically in FIG. 1) are aimed at separating statistically independent sources assumed to be stationary, centered and ergodic whereas those of the second family (F2) (reference [6], using the cyclic SOBI method) are designed to separate statistically independent sources assumed to be cyclostationary, centered and cycloergodic.
Two families of fourth-order separators are presently available. For example, those of the first family (F3) (reference [4] by J. F. Cardoso and A. Souloumiac, using the JADE method shown schematically in FIG. 2) are aimed at separating statistically independent sources assumed to be stationary, centered and ergodic while those of the second family (F4) (reference A. Ferreol and Chevalier [8] using the cyclic JADE technique) are designed to separate statistically independent sources assumed to be cyclostationary, centered and cycloergodic.
However, most of the sources encountered in practice are non-stationary and, more particularly, cyclostationary (with digitally modulated sources) and in certain cases deterministic (pure carriers). Furthermore, some of these sources are not centered. This is especially the case for deterministic sources and for certain digitally and non-linearly modulated sources as in the case of CPFSK sources with integer modulation index. This means that the empirical estimators of statistics classically used to implement the current techniques of self-learned source separation no longer have any reason to remain unbiased and consistent but are liable to become asymptotically biased. This may prevent the separation of the sources as shown in the document by A. Ferreol and P. Chevalier [5] for centered cyclostationary sources (linear digital modulations).